Response Surface Methodology for FCAW for Best Weld ......welding shop is the shop which moves the...

Research Article Open Access

Nazir and Sheikh, J Material Sci Eng 2019, 8:2

Research Article Open Access

Journal of Material Sciences & Engineering Jo

urna

l of M

aterial Sciences &Engineering

ISSN: 2169-0022

Volume 8 • Issue 2 • 1000518J Material Sci Eng, an open access journalISSN: 2169-0022

Response Surface Methodology for FCAW for Best Weld Condition on Desirability FunctionKashif Nazir* and Anwar Khalil SheikhDepartment of Mechanical Engineering, King Fahd University of Petroleum and Minerals Dhahran, Kingdom of Saudi Arabia

AbstractLosses to society in term of life and property due to catastrophic failure of pressure parts are often traced to

defective welds. Whereas the code/standard are collaborative experiences from welders and consumers voice which have recommended broad process parameters tolerance for the acceptable product (weld) quality but within that recommended window still, there is a scope to find the further narrowed down optimum or near optimal process parameters settings and authors researched narrowed down the code acceptable limits to find the best weld condition.

Moreover, major advances and researches are done in welding science and technology in recent years where researchers and scientists have focused mostly on welding works which were executed in the welding shop. The author studied on the FCAW which is widely used for welding in a remote area such as welding in the windy and dusty environment for cross country pipelines where the arrangement of welding shop is not economically feasible.

Fabricators and contractors consistently report that their welding issues are the need to reduce the welding costs due to rework of defected welds and to improve the welding productivity. The research is made to optimize the FCAW base on industries problems.

*Corresponding author: Kashif Nazir, Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals Dhahran, Kingdom of Saudi Arabia, Tel: 0097339253640; E-mail: [email protected]

Received April 13, 2019; Accepted April 22, 2019; Published April 30, 2019

Citation: Nazir K, Sheikh AK (2019) Response Surface Methodology for FCAW for Best Weld Condition on Desirability Function. J Material Sci Eng 8: 518.

Copyright: © 2019 Nazir K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords: Flux cored arc welding; Amperage; Arc length; Travelspeed; Response surface methodology; Optimization

IntroductionCombined shielding is obtained from outer gas-shielding and from

gasses produced from the arc. However, to make more convenient for field welding [1], Lincoln Electric invented the filler wire with a core which we filled with flux. In this way separate shielding gas is replaced by flux core wire moreover Lincoln Electric also declined to contribute to gas welding industry because Lincoln Electric used CO2 as shielding gas. In 1954 Arthur Bernard was the one who refined the process of dual shielding and flux and began the creation of Flux cored arc welding (FCAW) exactly the same as we are using in industries [2]. However, CO2 is still considered as additional protection of weld pool. Starting from 1959, self-shielding welding wire was available in the market which replaced the outer shielding with inner shielding during the welding process [2,3].

Gulhane et al. [4] produce research by analysis the major influence by means of Taguchi design of experiment philosophy that showed the effect on all factors. ANOVA was used to determine the influence input factors for a series of a run for FCAW process. Arivazhagan et al. [5] highlighted the importance of shielding gas composition for improved properties such toughness during welding by FCAW and he found that ideal gas mixture is argon (95%)+CO2 (5%) for welding with flux-core wire to achieve the desired value of toughness. Sterjovski et al. [6] studied with Artificial Neural Network (ANN) on the diffusible H2 amount and cracking proneness in welds welded by flux-core wire. Kannan and Murugan [7] showed study to investigate the properties of flux-core wire welding’s controllable factors on clad (CS to SDSS) parameters. All above investigation and experimental study were conducted on automatics flux-core welding process under a controlled environment such as well-established and equipped closed welding workshop whereas we have studied the semi-automatic flux core arc welding performed under tough environmental conditions such as windy and dusty in the mobile or outdoor welding shop. Through data mining of field engineering on nominal identical data on which we have done field study and relevant data points are chosen. The portable

welding shop is the shop which moves the location to location base on projects scope such as for cross-country pipeline.

Process Features and ParametersThe welding with flux-core wire has similarity with gas metal

arc welding; however, the main difference is information of welding consumables. The FCAW use solid wire as welding consumables whereas FACW uses flux cored wire as welding consumables. The reason for flux which is wrapped inside the core wire is only to provide shielding to protect the weld pool similar to flux protect the weld pool in Shield metal arc welding [1].

Voltage

The open circuit and welding arc voltages are critical variables in any SAW process affecting bead shape and penetration profile. The arc voltage also governs arc length beneath the flux layer and any change in arc length will radically alter weld metal composition mainly due to change in elements from the flux being alloyed into the weld (Figure 1).

Design of Experiment MethodologyThe factors can be classified as either continuous with low and

high value or categorical with different level [8,9]. We have selected continuous type of factors instead of the categorical type with minimum and maximum value because FCAW factor’s values fluctuate frequently due to welding in the temporary welding shop. Design of experiment is


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done by selecting current (A), wire feed (mm); heat Input (kJ/mm) and electrode extension (mm) as continuously controllable factors with low and high values as defined in Tables 1 and 2. These controllable factor’s effects are studied on four responses such as bead width (mm), weld reinforcement height (mm), weldment hardness (HB) and deposition rate (lb/hr). Each response is assigned with minimum and maximum values which are obtained from design condition for ASME B31.3 process piping for non-sour and non-lethal service and are mentioned in Table 1. The response’s results are studied and analyzed on mean values basis and target to minimize the mean of hardness and maximize the mean of deposition rate, reinforcement height, and bead width [9].

Design of experiment’s run and the model was established by response surface methodology which is used to refine the model after determination of important controllable factors using central composite design. CCD (factorial matrix+axial points+center points) is used because of it fit quadratic model with sequential experiments. The design is rotatable in composite design because it provides constant prediction at all points that are equidistant from the design center

which is placed in last. Below is the equation to calcuate the alpha:4 1No.of Corner Points 2 16 2.0

No.of AxialPoints 8

−Κ−1 ×

α = 2 × = =

The star points in rotatable center composite design are the distance of each axial point. Total 36 runs are designed with 12 center points per block. In below equation, author designates betas as the coefficient of linear, quadratic and interaction of input V (voltage), F (wire feed), S (travel speed), A (current), HI (Heat Input) and CTWD (cup to work piece distance). The β0 is the intercept term whereas β1, β2, β3, β4 and β11, β22, β33 β34, β14, β24 are the linear terms and interaction between variables terms respectively.

Y (responses)=β0+β1 (current)+β2 (wire feed)+β3 (heat-input)+β4 (CTWD)+β11 (current2)+β22 (wire feed2)+β33 (heat-input2)+β12 (current × wire feed)+β13 (current × heat-input)+β23 (wire feed × heat-input)+β34 (heat-input × CTWD)+β14 (current × CTWD)+β24 (wire feed × CTWD).

WIREFEED

CONTROL

WIRE FEEDDRIVE MOTOR

GASOUT

GUNCONTROL

CONTROLSYSTEM

WITHOUT GAS

WITH GAS

WORK LEAD

BASEMETAL ELECTRODE

LEAD

CONTRACTORCONTROL

VOLTAGECONTROL

ELECTRODEWIRE REEL

SHIELDING GASSOURCE

(OPTIONAL)

POWERSOURCE

100 V SUPPLY

Figure 1: Flux core arc welding process schematic diagram (courtesy of www.weldingis.com).

Name Units Analyze Objective Impact Sensitivity Low HighHardness HV Mean Minimize 3.0 Medium 160.0 230.0

Deposition rate lb/hr Mean Maximize 3.0 Medium 10.0 16.0Bead width mm Mean Maximize 3.0 Medium 8.0 14.0

Reinforcement mm Mean Maximize 3.0 Medium 2.0 4.0

Table 1: Defining of responses to be varied.

Name Units Type Role Low HighA:Current Amp Continuous Controllable 90.0 330.0

B:Wire feed mm/min Continuous Controllable 135.0 235.0C:Heat input kJ/mm Continuous Controllable 12.0 20.0

D:CTWD mm Continuous Controllable 12.0 25.0

Table 2: Defining of controllable factors to be measured.

Type of factors Design type Center Point Design is Randomized Total runsPer block Placement

Process Central composite design: 24+star

12 Last Yes 36=16+12+08Total Corner Points=24=16

Total Center Points=12Total Star Points=2 × n=2 × 4=8

Table 3: Design of experiments for a flux-cored arc welding process.

http://www.weldingis.com


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data is passed through significance test [9]. Since the observed response values and expected response values are different, it mean there are possibilities of either it happened only by chance (5%) or it happened by manipulation of controllable variables (95%). Therefore we start from null hypothesis, mean that the obtained variables do not bear any relation or significance with the responses and to check the null hypothesis rejection or correctness, p value is calculated and if the p value is less than alpha value, then it will reject the null hypothesis.

The analysis of variance is calculated for each of response and is explained in detail. Refer to Table 5 for hardness, Table 6 for deposition rate, Table 7 for bead width and Table 8 for reinforcement height.

R-squared is a statistical measure of how close the data are to the fitted regression line; 0% indicates that the model explains none of the variability of the response data around its mean; 100% indicates that the model explains all the variability of the response data around its mean. Value of R-squared is calculated to explain the fitted in the model which is 86.982185% of the variability in hardness. The value of

The DOE is summarized in Table 3 [9].

To fit the results of the experiment, the quadratic model of factors interaction is used and below table is the statistical data collected from large data points of the mobile welding shop, 32 runs are collected and responses of each run are measured from actual testing in mechanical laboratory (Table 4). The welding was conducted on API 5L Gr. 70 (fine grain normalized 10 mm thickness) material with single bevel angle. Bevel angle was checked with dye penetrant testing for any possible defects like crack etc. FCAW machine of model Lincoln “Idealarc CV400” was used for welding, whereas reinforcement was measured by using Cambridge type welding gauge.

The welding joint design (joint design and bead width and reinforcement height etc.) and the test coupons welded to get the responses on data matrix as per CCD of RSM are given in Figure 2a and 2b, respectively [2].

Analysis of the Experiments ResultsAfter welding data matrix is created through CCD of RSM, then

Controllable factors Responses received from welded work piecesF1 F2 F3 F4 R1 R2 R3 R4

Run Current Wire feed Heat input CTWD Hardness of weldment Deposition rate Bead width ReinforcementUnit Amp mm/min KJ/mm mm HV lb/hr mm mm

1 330 235 12 12 175.5 11 10 22 330 235 20 12 190 15 12 33 90 235 20 12 171 13 9 44 210 185 16 20 188 12 10 35 90 135 20 12 162 13 8 46 210 285 16 18.5 195 12 10 37 210 185 24 18.5 198 14 11 3.58 90 135 20 25 168 13 8 49 300 185 16 18.5 210 13 13 2.8

10 450 185 16 18.5 205 14 10 311 90 135 12 12 165 10 9 2.512 90 235 20 25 175 13 9 313 210 185 16 20 181 12 12 3.514 210 185 16 18.5 177 12 11 315 330 235 20 25 225 15 14 316 330 135 12 12 185 12 11 417 330 135 20 12 212 14 12 318 330 135 12 25 165 11 10 319 90 135 12 25 166 11 10 2.520 90 235 12 12 178 10 9 2.521 210 185 12 18.5 187 10 12 222 90 235 12 25 178 11 10 2.523 330 235 12 25 180 12 13 3.524 330 135 20 25 225 15 12 425 210 185 16 18.5 181 11 11 326 210 185 16 18.5 177 11 11 327 210 185 16 18.5 181 11 11 328 210 185 16 18.5 177 12 11 329 210 185 16 18.5 181 11 11 330 210 185 16 18.5 177 11 11 331 210 185 16 18.5 181 11 11 332 210 185 16 18.5 177 11 11 333 210 185 16 18.5 181 12 11 334 210 185 16 18.5 177 11 11 335 210 185 16 18.5 185 11 11 336 210 185 16 18.5 180 11 11 3

Table 4: DOE data matrix based on CCD (factorial matrix+axial points+center points).


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R-squared is adjusted as 78.303642% and the value is most suitable for comparing models with a different independent response. In the same way, the standard error is 7.13 and MAE are 3.88 which is the average value of the residuals.

(Sumof Square Sumof squareof Error)R - Squared = 1- 86.90%TotalSumof Square

−=

2(1 R ).(n 1)Adjusted R - Squareddue todegreeof freedom = 1- 79%n k 1− −

=− −

A

B

rh

Base plate

w Depositedmetal

w : Bead width h : Bead height r : Reinforcement

Photos of 1st Set of Test Coupons

a

b

c

d

abcd

====

7034 mm3.5 mm2 mm

o

Figure 2: (a) Joint design detail used for FCAW welding. (b) Test coupon welded to get the data matrix as per CCD of RSM.

Factors/interaction Sum of squares Degree of freedom (df1) Mean Square1 F-Ratio2 p value3

A:Current 2619.0604 1 2619.0604 51.51 0.0000B:Wire feed 37.904121 1 37.904121 0.75 0.3977C:Heat input 985.04658 1 985.04658 19.37 0.0002

D:CTWD 124.98295 1 124.98295 2.46 0.1319AA 0.18639344 1 0.18639344 0.00 0.9523AB 206.64063 1 206.64063 4.06 0.0568AC 1550.3906 1 1550.3906 30.49 0.0000AD 28.890625 1 28.890625 0.57 0.4593BB 79.676263 1 79.676263 1.57 0.2244BC 83.265625 1 83.265625 1.64 0.2146BD 118.26563 1 118.26563 2.33 0.1421CC 16.460496 1 16.460496 0.32 0.5754CD 328.51563 1 328.51563 6.46 0.0190DD 28.868497 1 28.868497 0.57 0.4595

Total error (df2) 1067.6805 21 50.841929Total (Corr.) 8201.6875 35

1 Mean square =Sum of square/df; 2 F-Ratio=Mean square/Total error; 3 p value=F. Dist (F-Ratio, df1,df2, False)R-squared value=86.982185%; R-squared after adjustment for degree of freedom=78.303642%; Standard error of estimated=7.1303527; Mean absolute error=3.881992

Table 5: Variance’s analysis for hardness.


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Factor/Interaction Sum of squares Degree of freedom Mean square F-Ratio p valueA:Current 8.3005897 1 8.3005897 29.85 0.0000

B:Wire Feed 0.066144076 1 0.066144076 0.24 0.6308C:Heat input 36.882212 1 36.882212 132.63 0.0000


Total error 5.8396424 21 0.27807821Total (Corr.) 70.0 35

R-squared value=91.657654%; R-squared after adjustment for degree of freedom=86.09609%; Standard error of estimated=0.52733121; Mean absolute error=0.34372867

Table 6: Variance’s analysis for deposition rate.

Factors/Interaction Sum of squares Degree of freedom Mean square F-Ratio p valueA:Current 32.399373 1 32.399373 80.72 0.0000

B:Wire feed 2.2410869 1 2.2410869 5.58 0.0279C:Heat input 0.026391575 1 0.026391575 0.07 0.8001


Total error 8.4291227 21 0.4013868Total (Corr.) 60.75 35


Table 7: Variance’s analysis for bead width.

Factors/Interaction Sum of squares Degree of freedom Mean square F-Ratio p valueA:Current 0.011056323 1 0.011056323 0.08 0.7805

B:Wire Feed 0.76015143 1 0.76015143 5.48 0.0292C:Heat input 2.7666318 1 2.7666318 19.94 0.0002


Total error 2.9141605 21 0.13876955Total (corr.) 8.6430556 35


Table 8: Variance’s analysis for reinforcement.


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The variability in hardness is obtained by ANOVA table and is mentioned separately for all effects. By comparison between mean square with experimental error (estimated), the statistical significance is calculated. If the p value is less than 0.05 then the response or their interaction are significant and in our analysis, we found four cases where p value is less than 0.05 and are highlighted in the table with red color and it is the indicating factor for significantly different from 0 at 95% confidence level.

Based on analysis of variance through ANOVA, the significant controllable factors are identified and then plot against the standardized effect. Figure 3 shows that current, heat input, the quadratic effect of current and heat input have a major effect on hardness value whereas the other factors are ignored from Pareto chart and from the further process. Figure 3 shows only factors with p value 0.2% but we considered factors whose p value are less than 0.05%.

In an analysis of variance for deposition rate, we have found 4 interaction which is current, heat input and AA and DD effects the deposition because p values is smaller than 0.05, at the 95% confidence level. The value of R-Squared is 91.6% and adjusted R-squared value is 86.09% and standard error of test is 0.5 and mean absolute error is 0.343. Based on analysis of variance through ANOVA, the significant controllable factors are identified and then plot against the standardized effect. Figure 4 shows that current, heat input, the quadratic effect of current and heat input have a major effect on deposition rate whereas the other factors are ignored from Pareto chart and from the further process. Figure 4 shows only factors with p value 0.4% but we considered factors whose p value are less than 0.05%.

In an analysis of variance for bead width, we have found 7 interaction which is current, wire feed, AA, AC, BB, BD etc., effects the bead width because p value is smaller than 0.05, at the 95% confidence level. The value of R-Squared is 86.12% and adjusted R-squared value is 76.87% and standard error of estimated is 0.6 and mean absolute error is 0.346. Base on analysis of variance through ANOVA, the significant controllable factors are identified and then plotted against the standardized effect. Figure 5 shows that current, wire feed and CTWD and their combined interaction have a major effect on bead width. Whereas other factors are ignored from Pareto chart and from the further process. Figure 5 shows only factors with p value 0.4% but we considered factors whose p value are less than 0.05%.

In an analysis of variance for reinforcement height, we have found 3 interaction which is current, wire feed and AC effects the reinforcement height because p values is smaller than 0.05, at the 95% confidence level. The value of R-squared is 66.28% and adjusted R-squared value is 43.80% and standard error of estimated is 0.3 and means the absolute error is 0.18288. Based on analysis of variance through ANOVA, the significant controllable factors are identified and then plotted against the standardized effect. Figure 6 shows that current, wire feed and AC have a major effect on reinforcement height. Whereas, the other factors are ignored from Pareto chart and from the further process. Figure 5 shows only factors with p value 0.3% but we considered factors whose p values are less than 0.05%.

Optimization through Desirability FunctionOptimum factors setting values are obtained through the value of

A:Current

AC

C:Heat input

CD

AB

BD

BC

0 2 4 6 8Standardized effect

Standardized Pareto Chart for Hardness+0

+-

Figure 3: Standardized Pareto chart for hardness.

C:Heat input

A:Current

DD

AA

D:CTWD

AC

BB

0 3 6 9 12 15Standardized effect

Standardized Pareto Chart for Deposition Rate+0

+-

Figure 4: Standardized Pareto chart for deposition rate.


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optimized desirability which is obtained by considering the combined results of all five responses for specific predicted values [9,10]. For optimization of response values, we have selected confident level of 95%. Whereas predicted values of each response is a mean value of upper 95% and lower 95% limit. Observed desirability is calculated from observed values of all response values for each run of an experiment by using Derringer’s model. Whereas predicted desirability is obtained from predicted values of all response base on lower and upper 95% limits of the confidence level.

Desirability function

The desirability function and loss function are used optimized approaches and we have used desirability function methodology because it is more applicability and flexibility as compared to loss function [11-14]. As per definition is given by Harrington [11], “converts every response value into scale-free value is known as desirability”. The desirability function was also studied and shared by Derringer and Suich’s [12] which is used for nominal-the-best (NTB), larger-the-best (LTB), smaller-the-best (STB) type of measurable responses. In our DOE, we have selected the larger-the-best which is defined as:

( )

0,

,

1,

ˆyl LSLi

Yl USLi LSL yl USLid i LSLi USLiyl USLi

=<

− < <= − =>

Where di is the desirability function, yet is the response, USLi is the upper 95% limit and LSLi is the lower 95% limits.

Table 9 shows all calculation where responses are optimized by getting their prediction by taking the mean of lower 95% limits and upper 95% limit for examples the optimized predicted values for hardness, deposition rate, bead width and reinforcement rate are 178.4, 15.05, 10.96 and 3.5, respectively. Based on Derringer and Suich’s desirability function as defined above, the desirability is calculated for

hardness, deposition rate, bead width, and reinforcement height are 73.5 %, 84.23%, 49.34% and 75.81%, respectively [9].

The overall desirability or optimized desirability is equaled 70%, which is obtained by considering the desirability of all responses then taking each value to the power equal to its impact, taking the product of both results, and the resultant product raises to a power equal to 1 divided by impact summation. A result is a number between 0 and 1, with more weight given to the response with the higher impact.

Composite Desirability=[d10.25.d20.25.d30.25.d40.25]1/1=(0.7359)0.25(0.8423)0.25+(0.49341)0.25+(0.7580)0.25=0.72=72%

Optimum setting of factors are obtained based on optimized desirability vs optimized responses values and are given in Table 10 and the graphical representation is mentioned in Figures 6, 7a and 7b.

Validation and DiscussionAfter getting the optimal values for factors next stage was to validate

these values refer to Figure 7c. In order to do this final weld, the run was conducted by using these optimal values obtained during these analyses. Welding was performed under same circumstances using the same material. Final results were obtained and found very close to the optimal responses as mentioned in Table 11.

Here hardness testing was performed by using portable hardness tester of model Reichert K2662 and tensile testing was performed by using UTS machine of Galdabini model Sun60-V630 whereas, reinforcement was measured by using cambridge type welding gauge

A:Current

AAAC

BBBD

B:Wire Feed

D: CTWDDDCD

BC


Standardized Pareto Chart for Bead Width+0

+-

Figure 5: Standardized Pareto chart for bead width.

Response Optimized Prediction Lower 95% limit

Upper 95% limit

Desirability

Hardness yes 178.48607 161.74129 195.23086 0.73591323Deposition

Rateyes 15.054339 14.317798 15.790879 0.84238979

Bead Width yes 10.960504 9.4417419 12.479266 0.49341736Reinforcement yes 3.5161812 2.8917234 4.1406389 0.75809058

Table 9: Optimum response values.

Factor SettingCurrent 216.62446

Wire feed 266.99596Heat input 23.999996

CTWD 12.000014

Table 10: Factor settings at optimum.

Optimized factors Optimized responses Actual results (Responses)

Current=216.62446 amp Hardness=178.48607 Hardness=170Wire feed=266.99595 mm Deposition rate=15.05339 lb/hr Deposition rate=15.5 lb/hrCTWD=12.0000014 mm Bead width=10.960504 mm Bead width=11 mmHeat input=23.999996 KJ/mm

Reinforcement=3.5161812 mm Reinforcement=3.3 mm

Table 11: Comparison between optimized and actual results.


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ConclusionStudy highlight the need of narrow down the acceptance tolerance

window as provided in International standard to reduce the rework and cost .Study explore the region for the best among the best weld condition based on multicriteria optimization. Study highlight the attempt to going through the international manufacturer standards by composite design and Box Behnken for the outdoor welding condition. Study was conducted by deploying response surface methodology on real life data collected from production line.

The above framework of the analyses can be applied for any such welding case and can be combined in a single software package to find optimal weld conditions leading to enhanced weld quality.

Acknowledgment

The completion of this research work could not be possible without support and assistance of so many people whose names may not all be enumerated, however, we would like to express our thanks to particular Mr. Ali Raza Lone, QC Manager of Saudi Arkad; Mr. Azhar, Ph. D. student of KFUPM; Mr. Saravanan, JGC Department Manager of Construction and the most Mr. ITO Kenji, Senior

C:Heat input

AC

B:Wire Feed

BB

AD

AB

CC


Standardized Pareto Chart for Reinforcement+0

+-

Figure 6: Standardized Pareto chart for reinforcement height.

A B

C

320

280

240

200

160

120

80-30 70 170 270 370 470

Current

Wire

Fee

d

Desirability PlotHeat input=23.999984,CTWD=12.000002

0.6 0.5 0.4 0.3 Desirability

Des

irabi

lity

0.00.10.20.30.40.50.60.70.80.91.0

Wire Feed

Desirability PlotHeat input=23.999984,CTWD=12.000002

1

0.8

0.6

0.4

0.2

0-30 70 170 270 370 470 80 120160200240280320

Figure 7: (a) Optimum factors setting at the optimum desirability, (b) Optimum factors setting at the optimum desirability, (c) Test coupon for validation.

Total samples = 3 FCAW + 3 GMAWHardness Locations = 4 WM + 12 HAZ + 4BMMaximum hardness of WM = 170 HV

Figure 8: Hardness testing for the validated test coupon.

and bead width with a simple ruler (Figure 8).


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Manager, JGC for their endless support during my research work. The research work’s abstract of same methodology on FCAW was also presented in “15th Annual Congress on Materials Research & Technology” held during February 19-20, 2018 in Paris, France.

Compliance with Ethical Standards

All procedure performed in our research involving human participants are in accordance with ethical standards of the KFUPM and Govt. of KSA.

Conflict of Interest

The authors declare that they have no conflict of interest.

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